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Implement a recurring HITL model evaluation workflow with Label Studio to keep a pulse on what’s really happening with your GenAI/ML models in production.
You’ve already evaluated your LLM or trained and validated your ML model for quality, but there are a range of things that can happen in production, from weird model behaviors to unexpected human inputs, not to mention the impacts of our ever changing world on a model’s context.
And while there are many tools on the market that can help monitor model drift, as a data scientist or researcher, there’s no comparable replacement for having a human understanding what’s really going on once a model is in production.
Join us for a technical webinar, where ML Evangelist Micaela Kaplan will demonstrate how to implement a recurring model evaluation workflow with Label Studio. Learn how to seamlessly integrate human-in-the-loop (HITL) monitoring into your production pipeline, ensuring your GenAI/ML models maintain peak performance over time. She’ll show you how to:
The workflow outlined in this webinar will give you and your team confidence that you are safeguarding the quality of your models while providing a path for model retraining and improvement. Don’t miss out, register now!
Machine Learning Evangelist
Micaela Kaplan is the Machine Learning Evangelist at HumanSignal. With her background in applied Data Science and a masters in Computational Linguistics, she loves helping other understand AI tools and practices.
Sr. Product Manager